






'''simple docstring'''





def __UpperCAmelCase   (					A					:			list			,       A					:			int			,       A					:			int = 0			,       A					:			int = 0		)    ->   int:
			UpperCAmelCase_      :       List[Any]          =       right or len(A		) - 1
			if left > right:
						return -1
			elif list_data[left] == key:
						return left
			elif list_data[right] == key:
						return right
			else:
						return search(A			,       A			,       left + 1			,       right - 1		)


if __name__ == "__main__":
						import doctest

						doctest.testmod()










'''simple docstring'''





import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_UpperCamelCase						:						List[str]  =					logging.getLogger(__name__)

_UpperCamelCase						:						int  =					'pytorch_model.bin'


@dataclasses.dataclass
class  snake_case__      :
					a_        =					dataclasses.field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."})
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}     ,				)






@dataclasses.dataclass
class  snake_case__      :
					a_        =					dataclasses.field(metadata={"help": "A csv or a json file containing the training data."})
					a_        =					dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."})
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "A csv or a json file containing the validation data."})
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "The name of the task to train on."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "The list of labels for the task."})






@dataclasses.dataclass
class  snake_case__      :
					a_        =					dataclasses.field(
					    metadata={"help": "The output directory where the model predictions and checkpoints will be written."})
					a_        =					dataclasses.field(
					    default="accuracy"     ,				metadata={"help": "The evaluation metric used for the task."})
					a_        =					dataclasses.field(
					    default="no"     ,				metadata={
					        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
					    }     ,				)
					a_        =					dataclasses.field(
					    default=10     ,				metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}     ,				)
					a_        =					dataclasses.field(
					    default=0.0     ,				metadata={
					        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
					    }     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}     ,				)
					a_        =					dataclasses.field(
					    default=0.0     ,				metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}     ,				)
					a_        =					dataclasses.field(
					    default=100     ,				metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Random seed for initialization."}     ,				)






def __UpperCAmelCase   (					A					:			str			,       A					:			Optional[Any]			,       A					:			List[Any]			,       A					:			Any			,       A					:			Union[str, Any]			,       A					:			Dict		)    ->   int:
			UpperCAmelCase_      :       Tuple          =       datasets.concatenate_datasets([infer_input, infer_output]			,       axis=1		)

			if args.do_filter_by_confidence:
						UpperCAmelCase_      :       Tuple          =       dataset.filter(lambda A		: example["probability"] > args.confidence_threshold		)

			if args.do_filter_by_val_performance:
						assert eval_result >= 0.0 and eval_result <= 1.0
						UpperCAmelCase_      :       Dict          =       int(eval_result * len(A		)		)
						print(A		)
						UpperCAmelCase_      :       Optional[int]          =       dataset.sort('''probability'''			,       reverse=A		)
						UpperCAmelCase_      :       Optional[Any]          =       dataset.select(range(A		)		)

			UpperCAmelCase_      :       Dict          =       dataset.remove_columns(['''label''', '''probability''']		)
			UpperCAmelCase_      :       List[Any]          =       dataset.rename_column('''prediction'''			,       '''label'''		)
			UpperCAmelCase_      :       List[str]          =       dataset.map(lambda A		: {"label": idalabel[example["label"]]}		)
			UpperCAmelCase_      :       Union[str, Any]          =       dataset.shuffle(seed=args.seed		)

			UpperCAmelCase_      :       str          =       os.path.join(A			,       F"train_pseudo.{args.data_file_extension}"		)
			if args.data_file_extension == "csv":
						dataset.to_csv(A			,       index=A		)
			else:
						dataset.to_json(A		)







def __UpperCAmelCase   (					A					:			Any			,       A					:			int			,       A					:			Union[str, Any]			,       A					:			Dict			,       **A					:			Any		)    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       Accelerator()
			# Make one log on every process with the configuration for debugging.
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''			,       datefmt='''%m/%d/%Y %H:%M:%S'''			,       level=logging.INFO			,       )
			logger.info(accelerator.state		)

			# Setup logging, we only want one process per machine to log things on the
			# screen. accelerator.is_local_main_process is only True for one process per
			# machine.
			logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR		)

			if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_info()
			else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()

			UpperCAmelCase_      :       Tuple          =       STModelArguments(model_name_or_path=A		)
			UpperCAmelCase_      :       int          =       STDataArguments(train_file=A			,       infer_file=A		)
			UpperCAmelCase_      :       Optional[Any]          =       STTrainingArguments(output_dir=A		)
			UpperCAmelCase_      :       Optional[int]          =       argparse.Namespace()

			for arg_class in (model_args, data_args, training_args):
						for key, value in vars(A		).items():
									setattr(A			,       A			,       A		)

			for key, value in kwargs.items():
						if hasattr(A			,       A		):
									setattr(A			,       A			,       A		)

    # Sanity checks
			UpperCAmelCase_      :       List[str]          =       {}
			UpperCAmelCase_      :       Any          =       None

			# You need to provide the training data and the data to predict on
			assert args.train_file is not None
			assert args.infer_file is not None
			UpperCAmelCase_      :       List[Any]          =       args.train_file
			UpperCAmelCase_      :       Optional[int]          =       args.infer_file

			if args.evaluation_strategy != IntervalStrategy.NO.value:
						assert args.eval_file is not None
						UpperCAmelCase_      :       List[str]          =       args.eval_file

			for key in data_files:
						UpperCAmelCase_      :       Dict          =       data_files[key].split('''.'''		)[-1]
						assert extension in ["csv", "json"], F"`{key}_file` should be a csv or a json file."
						if args.data_file_extension is None:
									UpperCAmelCase_      :       Any          =       extension
						else:
									assert extension == args.data_file_extension, F"`{key}_file` should be a {args.data_file_extension} file`."

			assert (
			    args.eval_metric in datasets.list_metrics()
			), F"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."

			# If passed along, set the training seed now.
			if args.seed is not None:
						set_seed(args.seed		)

			logger.info('''Creating the initial data directory for self-training...'''		)
			UpperCAmelCase_      :       Any          =       F"{args.output_dir}/self-train_iter-{{}}".format
			UpperCAmelCase_      :       Any          =       data_dir_format(0		)

			if accelerator.is_main_process:
						if args.output_dir is not None:
									os.makedirs(args.output_dir			,       exist_ok=A		)
									os.makedirs(A			,       exist_ok=A		)
			accelerator.wait_for_everyone()

			UpperCAmelCase_      :       Union[str, Any]          =       None
			UpperCAmelCase_      :       Tuple          =       None
			UpperCAmelCase_      :       Optional[Any]          =       0
			UpperCAmelCase_      :       int          =       False
			# Show the progress bar
			UpperCAmelCase_      :       int          =       tqdm(range(args.max_selftrain_iterations		)			,       disable=not accelerator.is_local_main_process		)

			# Self-train
			for iteration in range(0			,       int(args.max_selftrain_iterations		)		):
						UpperCAmelCase_      :       str          =       data_dir_format(A		)
						assert os.path.exists(A		)

						# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
						# iteration > 0
						UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''stage-1'''		)
						UpperCAmelCase_      :       Dict          =       {
						    '''accelerator''': accelerator,
						    '''model_name_or_path''': args.model_name_or_path,
						    '''cache_dir''': args.cache_dir,
						    '''do_train''': True,
						    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
						    '''do_eval''': True if args.eval_file is not None else False,
						    '''eval_file''': data_files['''eval'''],
						    '''do_predict''': True,
						    '''infer_file''': data_files['''infer'''],
						    '''task_name''': args.task_name,
						    '''label_list''': args.label_list,
						    '''output_dir''': current_output_dir,
						    '''eval_metric''': args.eval_metric,
						    '''evaluation_strategy''': args.evaluation_strategy,
						    '''early_stopping_patience''': args.early_stopping_patience,
						    '''early_stopping_threshold''': args.early_stopping_threshold,
						    '''seed''': args.seed,
						}
						# Add additional training arguments
						for key, value in kwargs.items():
									if key not in arguments_dict and not hasattr(A			,       A		):
												arguments_dict.update({key: value}		)

						UpperCAmelCase_      :       int          =       os.path.join(A			,       '''best-checkpoint'''			,       A		)
						if os.path.exists(A		):
									logger.info(
									    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''			,       A			,       A			,       )
						else:
									logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''			,       A		)
									finetune(**A		)
									accelerator.wait_for_everyone()
									assert os.path.exists(A		)
									logger.info('''Self-training job completed: iteration: %d, stage: 1.'''			,       A		)

						if iteration > 0 and args.finetune_on_labeled_data:
									# Stage 2 (optional): fine-tuning on the original labeled data
									UpperCAmelCase_      :       Any          =       os.path.join(A			,       '''best-checkpoint'''		)
									UpperCAmelCase_      :       Optional[int]          =       os.path.join(A			,       '''stage-2'''		)
									# Update arguments_dict
									UpperCAmelCase_      :       List[Any]          =       model_path
									UpperCAmelCase_      :       Optional[int]          =       data_files['''train''']
									UpperCAmelCase_      :       Union[str, Any]          =       current_output_dir

									UpperCAmelCase_      :       Optional[int]          =       os.path.join(A			,       '''best-checkpoint'''			,       A		)
									if os.path.exists(A		):
												logger.info(
												    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''			,       A			,       A			,       )
									else:
												logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''			,       A		)
												finetune(**A		)
												accelerator.wait_for_everyone()
												assert os.path.exists(A		)
												logger.info('''Self-training job completed: iteration: %d, stage: 2.'''			,       A		)

						UpperCAmelCase_      :       Dict          =       iteration
						UpperCAmelCase_      :       Optional[Any]          =       data_dir_format(iteration + 1		)

						UpperCAmelCase_      :       Any          =       AutoConfig.from_pretrained(os.path.join(A			,       '''best-checkpoint'''		)		)
						UpperCAmelCase_      :       Optional[Any]          =       config.idalabel
						UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''eval_results_best-checkpoint.json'''		)
						UpperCAmelCase_      :       Union[str, Any]          =       os.path.join(A			,       '''test_results_best-checkpoint.json'''		)
						assert os.path.exists(A		)

						with open(A			,       '''r'''		) as f:
									UpperCAmelCase_      :       Optional[int]          =       float(json.load(A		)[args.eval_metric]		)
						UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''infer_output_best-checkpoint.csv'''		)
						assert os.path.exists(A		)
						# Loading the dataset from local csv or json files.
						UpperCAmelCase_      :       str          =       load_dataset(args.data_file_extension			,       data_files={'''data''': data_files['''infer''']}		)['''data''']
						UpperCAmelCase_      :       Union[str, Any]          =       load_dataset('''csv'''			,       data_files={'''data''': infer_output_file}		)['''data''']

						if accelerator.is_main_process:
									os.makedirs(A			,       exist_ok=A		)
									shutil.copy(A			,       os.path.join(A			,       F"eval_results_iter-{iteration}.json"		)		)
									if os.path.exists(A		):
												shutil.copy(A			,       os.path.join(A			,       F"test_results_iter-{iteration}.json"		)		)
									create_pseudo_labeled_data(A			,       A			,       A			,       A			,       A			,       A		)
						accelerator.wait_for_everyone()

						UpperCAmelCase_      :       int          =       os.path.join(A			,       F"train_pseudo.{args.data_file_extension}"		)

						if args.evaluation_strategy != IntervalStrategy.NO.value:
									UpperCAmelCase_      :       List[str]          =       eval_result

									if best_iteration is None:
												UpperCAmelCase_      :       Any          =       new_iteration
												UpperCAmelCase_      :       List[Any]          =       new_eval_result
									else:
												if new_eval_result - best_eval_result > args.early_stopping_threshold:
															UpperCAmelCase_      :       str          =       new_iteration
															UpperCAmelCase_      :       Optional[int]          =       new_eval_result
															UpperCAmelCase_      :       Any          =       0
												else:
															if new_eval_result == best_eval_result:
																		UpperCAmelCase_      :       int          =       new_iteration
																		UpperCAmelCase_      :       List[Any]          =       new_eval_result
															early_stopping_patience_counter += 1

												if early_stopping_patience_counter >= args.early_stopping_patience:
															UpperCAmelCase_      :       List[Any]          =       True

						progress_bar.update(1		)

						if should_training_stop:
									break

			if best_iteration is not None:
						# Save the best iteration
						logger.info('''Best iteration: %d'''			,       A		)
						logger.info('''Best evaluation result: %s = %f'''			,       args.eval_metric			,       A		)
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(A			,       F"eval_results_iter-{iteration}.json"		)			,       os.path.join(A			,       '''eval_results_best-iteration.json'''		)			,       )
			else:
						# Assume that the last iteration is the best
						logger.info('''Best iteration: %d'''			,       args.max_selftrain_iterations - 1		)
						logger.info('''Best evaluation result: %s = %f'''			,       args.eval_metric			,       A		)
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(A			,       F"eval_results_iter-{args.max_selftrain_iterations - 1}.json"		)			,       os.path.join(A			,       '''eval_results_best-iteration.json'''		)			,       )


